# LTXVideoTransformer3DModel

A Diffusion Transformer model for 3D data from [LTX](https://huggingface.co/Lightricks/LTX-Video) was introduced by Lightricks.

The model can be loaded with the following code snippet.

```python
from diffusers import LTXVideoTransformer3DModel

transformer = LTXVideoTransformer3DModel.from_pretrained("Lightricks/LTX-Video", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
```

## LTXVideoTransformer3DModel[[diffusers.LTXVideoTransformer3DModel]]

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<docstring><name>class diffusers.LTXVideoTransformer3DModel</name><anchor>diffusers.LTXVideoTransformer3DModel</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/transformer_ltx.py#L385</source><parameters>[{"name": "in_channels", "val": ": int = 128"}, {"name": "out_channels", "val": ": int = 128"}, {"name": "patch_size", "val": ": int = 1"}, {"name": "patch_size_t", "val": ": int = 1"}, {"name": "num_attention_heads", "val": ": int = 32"}, {"name": "attention_head_dim", "val": ": int = 64"}, {"name": "cross_attention_dim", "val": ": int = 2048"}, {"name": "num_layers", "val": ": int = 28"}, {"name": "activation_fn", "val": ": str = 'gelu-approximate'"}, {"name": "qk_norm", "val": ": str = 'rms_norm_across_heads'"}, {"name": "norm_elementwise_affine", "val": ": bool = False"}, {"name": "norm_eps", "val": ": float = 1e-06"}, {"name": "caption_channels", "val": ": int = 4096"}, {"name": "attention_bias", "val": ": bool = True"}, {"name": "attention_out_bias", "val": ": bool = True"}]</parameters><paramsdesc>- **in_channels** (`int`, defaults to `128`) --
  The number of channels in the input.
- **out_channels** (`int`, defaults to `128`) --
  The number of channels in the output.
- **patch_size** (`int`, defaults to `1`) --
  The size of the spatial patches to use in the patch embedding layer.
- **patch_size_t** (`int`, defaults to `1`) --
  The size of the tmeporal patches to use in the patch embedding layer.
- **num_attention_heads** (`int`, defaults to `32`) --
  The number of heads to use for multi-head attention.
- **attention_head_dim** (`int`, defaults to `64`) --
  The number of channels in each head.
- **cross_attention_dim** (`int`, defaults to `2048 `) --
  The number of channels for cross attention heads.
- **num_layers** (`int`, defaults to `28`) --
  The number of layers of Transformer blocks to use.
- **activation_fn** (`str`, defaults to `"gelu-approximate"`) --
  Activation function to use in feed-forward.
- **qk_norm** (`str`, defaults to `"rms_norm_across_heads"`) --
  The normalization layer to use.</paramsdesc><paramgroups>0</paramgroups></docstring>

A Transformer model for video-like data used in [LTX](https://huggingface.co/Lightricks/LTX-Video).




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## Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

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<docstring><name>class diffusers.models.modeling_outputs.Transformer2DModelOutput</name><anchor>diffusers.models.modeling_outputs.Transformer2DModelOutput</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/modeling_outputs.py#L21</source><parameters>[{"name": "sample", "val": ": torch.Tensor"}]</parameters><paramsdesc>- **sample** (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel) is discrete) --
  The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
  distributions for the unnoised latent pixels.</paramsdesc><paramgroups>0</paramgroups></docstring>

The output of [Transformer2DModel](/docs/diffusers/main/en/api/models/transformer2d#diffusers.Transformer2DModel).




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